ChIP-seq and mRNA-seq experiments were performed to understand the role of the CLAMP protein in dosage compensation ChIP-seq experiments compared the binding profiles of CLAMP in male and female cells and mRNA-seq data to define the role of CLAMP in regulating genes on the X-chromosome
ChIP-seq was performed to compare binding the genome-wide binding profile of the CLAMP transcription factor in two different Drosophila species. ChIPseq experiments compare the binding profile of CLAMP in female larvae to identify conservation of its binding sequence.
Contributors:Jian Zhou, Wangjie Yu, Paul E. Hardin
Contributors:Leighton J. Core, Joshua J. Waterfall, Daniel A. Gilchrist, David C. Fargo, Hojoong Kwak, Karen Adelman, John T. Lis
UCSC Browser Screenshot of Sarkosyl Dependent and NELF RNAi GRO-Seq Experiments, Related to Figure 2
TSS-RNA reads (Nechaev et al., 2010) marking TSSs are in dark blue for the plus and minus strand (read/base/10ˆ6 reads). GRO-seq reads (reads/base/10ˆ6 reads) aligning to the plus strand are shown in red; minus strand in blue. ChIP-seq for total Pol II (α-Rpb3) is shown in green (reads/25bp bin), and gene annotations are shown at the bottom in blue. The arrowheads depict TSSs and the ∗ denotes a TSS that is reannoated with our data sets.
... Pol II at Promoters Is Predominantly Engaged and Competent for Elongation
(A) Representative browser shot showing Pol II Chip-seq (green) and GRO-seq (red) with y axis in reads/bp/10exp6. The regions used for calculating the engaged and competent fraction (ECF) at promoters are indicated below.
(B) Schematic explaining the workflow used to calculate the ECF for Pol II at promoters.
(C) Histogram showing the distribution of ECF values for significantly bound promoters (n = 3,168). The vertical lines represent a 50% (black), the average (red), and the Hsp70 (green) ECFs. Promoters with the lowest ECFs are highlighted in purple.
(D) Boxplots showing Pol II ChIP-seq levels at promoters with different ranges of ECF. Promoters with the lowest (purple) and the highest (dark red) ECF values have less Pol II bound at promoters in ChIP-seq experiments than promoters with less extreme ECF values (middle 20% shown), suggesting that the ChIP and GRO discrepancies here could be due to experimental noise.
The box spans the first quartile (Q1, bottom) to third quartile (Q3, top), the horizontal line in the box represents the median, and the whiskers extend as follows: (Q1 or Q3 + 1.5 )∗(Q3-Q1). See also Figures S6 and S7.
... RNA Polymerase Distribution on mRNA-Encoding Genes Using GRO-Seq
(A) A representative view of GRO-seq data from S2 cells in the UCSC genome browser (Kent et al., 2002). GRO-seq reads (reads/base) aligning to the plus strand are shown in red; minus strand in blue. ChIP-seq for total Pol II (α-Rpb3) is shown in green (reads/25 bp bin), and gene annotations are shown at the bottom in blue.
(B) GRO-seq data aligned to transcription start sites (TSSs). For all genes, reads aligning to the sense strand of the gene are in red; antisense strand in blue. For nonbidirectional genes (head-to-head promoters within 1 kb removed), reads aligning to the sense strand of the gene are in green; antisense strand in orange.
(C) Comparison of directionality of Drosophila and human promoters. The distribution of the ratios of sense and antisense reads around promoters (log2) is plotted for active promoters (>25 reads) in IMR90 cells (green) and Drosophila S2 cells (blue). How different types of directionality of transcription from promoters are reflected in the ratio are indicated in italicized lettering.
(D) GRO-seq profiles from ±1.5 kb relative to TSS are shown for all human promoters (green, sense; orange, antisense) or human promoters that contain a TATA box (red, sense; blue, antisense).
(E) GRO-seq data aligned to gene end for all genes (red, sense; blue, antisense), and after convergent genes within 1.5 kb are removed (green, sense; orange, antisense).
See also Figures S1 and S2.
... Supporting Genomic Data at Enhancers in This Study, Related to Figure 4
(A) GRO-seq data at putative human enhancers (n = 34,915). GRO-seq data is from IMR90 cells (Core et al., 2008). Data was compiled relative to the center of DHS sites.
(B and C) Pol II ChIP-seq and NELF ChIP-chip data (Gilchrist et al., 2010), respectively, around Drosophila putative enhancers.
... Comparison between Assays that Detect Polymerase at Promoters and in Genes, Related to Figure 5
(A–C) Shown are scatter-plots comparing amount of sequencing reads between (A) GRO-seq and Pol II ChIP-seq, (B) small-RNA-seq to ChIP-seq, and (C) GRO-seq to small-RNA-seq at promoters. All unique promoters are shown in black (n = 12,541); promoters called Pol II-bound by ChIP-seq (n = 3,168) in red. rho is Spearman's correlation coefficient between the two data sets.
(D) Normalization between ChIP-seq and GRO-seq data sets through fitting of signal within gene bodies. Plotting of the signal for each assay within all genes (black) shows a poor correlation (rho = 0.54), whereas plotting the signal for each after selecting genes that are highly active gives (red) an excellent correlation between data sets (rho = 0.87). Highly active genes are classified as those with the top 10% of Ser2P ChIP (n = 1874) signal within the gene (mark of active polymerases). The fit of the gene data for highly active genes is shown in green. This equation is used for calculating the engaged, competent fraction of polymerase at promoters.
Contributors:Davie K, Jacobs J, Atkins M, Potier D, Christiaens V, Halder G, Aerts S
Genomic enhancers regulate spatio-temporal gene expression by recruiting specific combinations of transcription factors (TFs). When TFs are bound to active regulatory regions, they displace canonical nucleosomes, making these regions biochemically detectable as nucleosome-depleted regions or accessible/open chromatin. Here we ask whether open chromatin profiling can be used to identify the entire repertoire of active promoters and enhancers underlying tissue-specific gene expression during normal development and oncogenesis in vivo. To this end, we first compare two different approaches to detect open chromatin in vivo using the Drosophila eye primordium as a model system: FAIRE-seq, based on physical separation of open versus closed chromatin; and ATAC-seq, based on preferential integration of a transposon into open chromatin. We find that both methods reproducibly capture the tissue-specific chromatin activity of regulatory regions, including promoters, enhancers, and insulators. Using both techniques, we screened for regulatory regions that become ectopically active during Ras-dependent oncogenesis, and identified 3778 regions that become (over-)activated during tumor development. Next, we applied motif discovery to search for candidate transcription factors that could bind these regions and identified AP-1 and Stat92E as key regulators. We validated the importance of Stat92E in the development of the tumors by introducing a loss of function Stat92E mutant, which was sufficient to rescue the tumor phenotype. Additionally we tested if the predicted Stat92E responsive regulatory regions are genuine, using ectopic induction of JAK/STAT signaling in developing eye discs, and observed that similar chromatin changes indeed occurred. Finally, we determine that these are functionally significant regulatory changes, as nearby target genes are up- or down-regulated. In conclusion, we show that FAIRE-seq and ATAC-seq based open chromatin profiling, combined with motif discovery, is a straightforward approach to identify functional genomic regulatory regions, master regulators, and gene regulatory networks controlling complex in vivo processes. FAIRE-Seq in Drosophila wild type eye-antennal imaginal discs (2 wt strains); ATAC-Seq in Drosophila wild type eye-antennal imaginal discs (3 wt strains) ; FAIRE-Seq in Drosophila Ras/Scrib induced eye disc tumors (1 early and 1 late); ATAC-Seq in Drosophila Ras/Scrib induced eye disc tumors (1 early and 1 late); ATAC-Seq in Drosophila eye discs with Unpaired over-expression (2 biological replicates); CTCF ChIP-seq in Drosophila eye discs; ChIP-seq input in Drosophila eye discs
Contributors:Herz HM, Mohan M, Garrett AS, Miller C, Casto D, Zhang Y, Seidel C, Haug JS, Florens L, Washburn MP, Yamaguchi M, Shiekhattar R, Shilatifard A
This SuperSeries is composed of the following subset Series: GSE33546: Polycomb repressive complex 2-dependent and –independent functions of Jarid2 in transcriptional regulation in Drosophila [ChIP-Seq] GSE36038: Polycomb repressive complex 2-dependent and –independent functions of Jarid2 in transcriptional regulation in Drosophila [Affymetrix] Refer to individual Series
Contributors:Georgi K. Marinov, Jie Wang, Dominik Handler, Barbara J. Wold, Zhiping Weng, Gregory J. Hannon, Alexei A. Aravin, Phillip D. Zamore, Julius Brennecke, Katalin Fejes Toth
No Redistribution of Pol II over Transposons Is Observed in piwi Mutant Files
(A) Scatterplot displaying Pol II ChIP-seq RPM values versus input RPM values over consensus transposable elements in wild-type and piwi mutant flies.
(B) Shown are Pol II ChIP-seq and input RPM levels over the transposon consensus sequences of F-element and mdg3.
... The Huang et al. Data Processing Pipeline Generates Artificial Enrichment over Repetitive Regions
The Piwi ChIP-seq and input/background datasets were processed following the Huang et al. pipeline (”Piwi ChIP”). In addition, the pipeline was also run swapping the ChIP and the input, i.e., the control sample was treated as ChIP and vice versa, resulting in the “background” track.
(A) The fraction of signal mapping to transposable elements was calculated, revealing higher “enrichment” in the background than in the Piwi ChIP-seq dataset.
(B) Strong apparent enrichment over individual transposable elements was observed in the ChIP track (upper track), as reported by Huang et al., but also in the background track (lower track), and even over different portions of the same transposable element in both tracks (middle track), strongly arguing that the enrichment over transposable elements reported by Huang et al. is a computational artifact. Signal observed on individual copies correlates well with enrichment profiles when mapped to the consensus sequence of the respective transposons (shown below each track). Sequences showing “enrichment” in the background are indicated with gray blocks to depict the correlations between the signal on individual TE copies and the consensus sequence.
(C) Fraction of signal (calculated with the Huang et al. pipeline) mapping to transposable elements for the modENCODE transcription factor set.
... Piwi Is Not Enriched over Transposons in the Huang et al. Dataset
(A) Absence of enrichment in the Piwi ChIP-seq dataset and high enrichment of H3K9me3 (from Muerdter et al., 2013) over consensus transposons; each dot corresponds to a transposon consensus sequence.
(B) The concentration of Piwi signal over transposons in the Huang et al. dataset arises from failure to normalize multiply mapping reads. Shown is the region from Figure 2C of Huang et al. (2013). Top: Piwi ChIP-seq and background (input) data from Huang et al. showing (1) unique alignments; (2) all alignments, with reads normalized for mapping multiplicity; and (3) all alignments, with each alignment treated as a uniquely mapped read. Bottom: data processed per Huang et al. The enrichment of Piwi over repetitive elements is only observed when no multi-read normalization is applied and is seen in both ChIP and control datasets.
(C) The minimal Piwi ChIP-seq enrichment observed over some individual transposable elements is well within the range of experimental noise. Shown is the cumulative distribution function (CDF) of the ratio between total ChIP RPM and control/background RPM for each DNA, LINE, or LTR repetitive element (each dot represents an individual TE insertion). Piwi ChIP-seq data from Huang et al. (red) and H3K9me3 data from Muerdter et al. (blue) are plotted alongside the cumulative distribution for 11 transcription factor ChIP-seq datasets from modENCODE (gray), for which there is no expectation of enrichment at repetitive elements. Only repeat instances with at least 10 RPM in at least one of the ChIP and control datasets for each ChIP/background pairing were included. H3K9me3 showed high average enrichment over background at most of the elements in all three classes. In contrast, the Piwi ChIP-seq data were well within the range of the distributions for modENCODE transcription factors.
To understand the role of the conserved Sterile Alpha Motif of the Polycomb Group protein Polyhomeotic, the genome wide distribution of wild type and polymerization defective Polyhomeotic containing mutations in the SAM domain was determined by ChIP-SEQ. ChIP-seq Identification of polyhomeotic wild type (PH-WT) and mutant (PH-ML) binding sites.
Contributors:Rajprasad Loganathan, Joslynn S. Lee, Michael B. Wells, Elizabeth Grevengoed, Matthew Slattery, Deborah J. Andrew
Results from DAVID clustering analysis of GO terms for genes activated by Ribbon based on microarray data and bound by Ribbon in the salivary gland based on ChIP-Seq.
... ChIP-seq analysis identifies Rib binding sites in salivary gland cells. (A) Schematic outline of the experimental approach to identify SG-specific Rib binding sites. ChIP-seq datasets were obtained from samples using two different GAL4 constructs to drive expression of UAS-rib-gfp in the SG. The overlap of binding events observed with both drivers enriches for SG-specific Rib binding. (B) Rescue of the SG phenotype in the rib1/ribP7 mutant background with fkh-Gal4::UAS-rib-GFP verified the functionality of UAS-rib-gfp construct used in the ChIP-seq experiments. (C) Tissue expression of fkh-GAL4 and sage-GAL4 drivers spanning the stages used for the ChIP-seq analyses. Arrowheads indicate the SG at different developmental stages. (D) SG-enriched ChIP-seq signals correspond to Rib binding events in the vicinity of two Rib target genes – Hsp70Ba and Obp99b.
... Drosophila... Rib SG binding sites overlap with genes expressed in the SG and with genes whose expression changes in rib mutants based on microarray analysis. (A) Venn diagram representing the overlap of genes from the ChIP-seq (494 genes bound by Rib in the SG), microarray (774 genes activated by Rib in the whole embryo) and BDGP gene expression database (434 SG-enriched gene expression). (B) Venn diagram representing the overlap of genes from the ChIP-seq (494 genes bound by Rib in the SG), microarray (1176 genes repressed by Rib in the whole embryo) and BDGP gene expression database (434 SG-enriched gene expression). (C) In situ hybridization analysis of SG genes activated by Rib and with nearby Rib binding sites in rib1/ribP7 mutant and heterozygous (rib1/+ or ribP7/+) embryos. (D) In situ hybridization analysis of SG genes repressed by Rib and with nearby Rib binding sites, in rib mutant and heterozygous embryos. (E) In situ hybridization analysis of a SG gene with nearby Rib binding sites whose expression is not detectably changed in rib mutant compared with heterozygous embryos. Rib mutants were identified by morphological criteria and/or the absence of expression of lacZ from the ftz-lacZ containing balancer chromosomes. Black arrowheads indicate SGs and white arrowheads indicate lacZ expression from the balancer chromosome in C-E.
... Results from DAVID clustering analysis of GO terms for genes repressed by Ribbon based on microarray data and bound by Ribbon in the salivary gland based on ChIP-Seq.
... Microarray gene expression analysis indicates the direction of transcriptional control of Rib targets. (A) RNA was isolated from three individual samples each of stage 11–16 WT and rib1/ribP7 embryos. Volcano plot shows genes that were downregulated (blue) or upregulated (red) at least 1.5-fold (P0.05) are indicated by gray. (B, C) Venn diagrams representing the overlap of 494 genes from the ChIP-seq and microarray (774 targets activated and 1176 repressed by Rib, respectively) data sets are shown. (D) The set of transcripts that are downregulated (blue) or upregulated (red) at least 1.5-fold (PChIP-seq analyses) are marked (cyan). (E, F) qRT-PCR results for a subset of genes obtained from the overlap of ChIP-seq and microarray data confirms significant expression change in the same direction as observed with microarray analysis for all but two examples, Sema-5C and CLS. *P<0.01, **P<0.001, Mann–Whitney U test.
Contributors:David A. Orlando, Mei Wei Chen, Victoria E. Brown, Snehakumari Solanki, Yoon J. Choi, Eric R. Olson, Christian C. Fritz, James E. Bradner, Matthew G. Guenther
Normalization and Interpretation of ChIP-Seq Data
(A) Schematic representation of a typical ChIP-seq data workflow. Interrogation of a human epigenome (Blue circles, nucleosomes) with a full complement of histone modification (red circles, top) versus an epigenome with a half complement of histone modification (red circles, bottom). ChIP, sequencing, and mapping using reads per million (RPM) reveals ChIP-seq peaks (blue). A comparison of the peaks as a percentage of the total reads reveals little difference.
(B) Schematic representation of a ChIP-seq data workflow with reference genome normalization. Interrogation of a human epigenome (Blue circles, nucleosomes) with a full complement of histone modification (red circles, top) versus an epigenome with a half complement of histone modification (red circles, bottom). A fixed amount of reference epigenome (orange, nucleosomes; red, histone modifications) is added to human cells in each condition. After ChIP, sequencing, and mapping, the ChIP sequence reads are normalized to the percentage of reference genome reads in the sample (reference-adjusted RPM [RRPM]). A comparison of ChIP-seq signals using normalized reads reveals a 50% difference between peaks. This method is called ChIP with reference exogenous genome (ChIP-Rx).
... ChIP-Rx Reveals Quantitative Epigenome Changes
(A and B) Percentage of reads aligning to either test (human, blue) or Drosophila (reference, orange) genomes after H3K79me2 ChIP-Rx (A) or H3K4me3 ChIP-Rx (B). Samples containing 0%, 25%, 50%, 75%, or 100% EPZ5676 treated Jurkat cells were used as defined in Figure 2B.
(C and D) Sequenced reads from H3K79me2 (C) and H3K4me3 (D) immunoprecipitations at the RPL13A gene locus in traditional reads per million (RPM,top) or reference-adjusted reads per million (RRPM, bottom; see Experimental Procedures). Color indicates the percentage of sample treated with EPZ5676. The gene model is shown below the track.
(E) Meta-gene profile of H3K79me2-occupied genes in Jurkat cells. Meta-gene profiles were produced with traditional RPM (left) or RRPM (right). Color indicates the percentage of Jurkat cell sample treated with EPZ5676 as in Figure 2B. Region −5 to +10 kb around the transcription start site (TSS) is shown. Meta-gene profile was derived from top 5,000 protein-coding genes as defined by total H3K79me2 signal in the 0% treated (untreated with EPZ5676) sample. A meta-gene profile representing all genes is shown in Figure S3.
(F) Meta-gene profile of H3K4me3-occupied genes in Jurkat cells. Meta-gene profiles were produced with traditional RPM (left) or RRPM (right). Color indicates the percentage of Jurkat cell sample treated with EPZ5676 as in Figure 2B. Region −5 to +10 kb around the transcription start site (TSS) is shown. Meta-gene profile was derived from top 5,000 protein-coding genes as defined by total H3K4me3 signal in the 0% treated (untreated with EPZ5676) sample. A meta-gene profile representing all genes is shown in Figure S3.
(G and H) Line graphs display the observed fold-change difference in average meta-gene signal across the −5 to +10 kb window around the TSS for each H3K79me2 (G) or H3K4me3 (H) ChIP sample (x axis) relative to the signal from the 0% treated population using traditional (gray) or reference (black) normalization.
See also Figures S2–S4 and Table S2.
... ChIP-Rx Reveals Epigenomic Alterations in Disease Cells that Respond to Drug Treatment
(A) Western blot showing the levels of H3K79me2 in MV4;11 cells after treatment for 4 days with increasing concentrations of EPZ5676.
(B) Percentage of H3K79me2 ChIP-seq reads aligning to either test (human, blue) or Drosophila (reference, orange) genomes after H3K79me2 ChIP-Rx from MV4;11 cells treated as in (A).
(C) Sequenced reads from H3K79me2 immunoprecipitations at the REXO1 gene locus in standard RPM (top) or RRPM (bottom) (see Experimental Procedures). Color indicates the concentration of EPZ5676 given to each sample. The gene model is shown below the track.
(D) Meta-gene profile of H3K79me2-occupied genes in MV4;11 cells. Meta-gene profiles were produced with traditional Reads Per Million (RPM, left) or Reference-adjusted Reads Per Million (RRPM, right). Color indicates the concentration of EPZ5676 used in each sample. The region −5 kb to +10 kb around the TSS is shown. Meta-gene profile was derived from top 5,000 protein-coding genes as defined by total H3K79me2 signal in the 0nM treated (untreated with EPZ5676) sample. A meta-gene profile representing all genes is shown in Figure S3.
(E) Line graph displays the observed fold-change difference in average meta-gene signal across the −5 to +10 kb window around the TSS for each H3K79me2 ChIP sample (x axis) relative to the signal from the 0 nM treated population using standard (gray) or reference (black) normalization.
(F) Box plots display the distribution of the observed fold change of H3K79me2 signal −5 kb to +10 kb around the TSS of all genes between the 0 nM and 5 nM treated samples (blue, MV4;11; green, Jurkat) for all genes using traditional (left) or reference-adjusted (right) normalization (see the Supplemental Experimental Procedures).
See also Figures S3 and S5 and Table S2.
... Experimental Design of Differential H3K79me2 Detection
(A) Schematic representation of differential H3K79me2 detection and normalization strategies. Two populations of cells were produced: a human epigenome (blue nucleosomes) with a full complement of H3K79me2 (red circles, top left) and a human epigenome (blue nucleosomes) with depleted H3K79me2 due to EPZ5676 exposure (top right). These cells were mixed in defined proportions in order to allow a dilution of total genomic histone modification (dark red to pink). Cell mixtures were subjected to ChIP-seq in the presence of the reference Drosophila epigenome (orange). ChIP-seq signals were calculated based on traditional or Drosophila-reference-normalized methods. See also Figure S1.
(B) Western blot validation of H3K79me2 depletion in Jurkat cells. Mixtures of 0%–100% EPZ5676-treated cells (0:100; 25:75; 50:50, 75:25; 100:0 proportions of [DMSO-treated:EPZ5676-treated] cells) were measured by immunoblot (IB) for the presence of H3K79me2, H3K4me3, or total histone H3 (loading control). Treated cells were exposed to 20 μM EPZ5676 for 4 days.
See also Table S1.